Dynamic Peer Groups of Arbitrage Characteristics
提出一个基于半参数特征错误定价和因子载荷函数的资产定价因子模型，通过增强检验和聚类分析发现美国股市中存在错误定价函数，且相同特征的不同聚类产生相似套利收益，形成套利特征的“同行群体”。
We propose an asset pricing factor model constructed with semiparametric characteristics-based mispricing and factor loading functions. We approximate the unknown functions by B-splines sieve where the number of B-splines coefficients is diverging. We estimate this model and test the existence of the mispricing function by a power enhanced hypothesis test. The enhanced test solves the low power problem caused by diverging B-splines coefficients, with the strengthened power approaching one asymptotically. We also investigate the structure of mispricing components through Hierarchical K-means Clusterings. We apply our methodology to CRSP (Center for Research in Security Prices) and Compustat data for the U.S. stock market with one-year rolling windows during 1967–2017. This empirical study shows the presence of mispricing functions in certain time blocks. We also find that distinct clusters of the same characteristics lead to similar arbitrage returns, forming a “peer group” of arbitrage characteristics.